DANN & GAIN Hybrid Model

• Generative Adversarial Imputation Networks (GAIN) for missing values imputation
• Domain-Adversarial Neural Networks (DANN) for domain-aware learning and classification.

Model Architecture

• Encoder: Maps input to latent space
• Generator & Discriminator: For adversarial imputation
• Gradient Reversal Layer: Enables domain adversarial training
• Domain Classifier: Predicts domain labels
• Decoder: Reconstructs inputs after imputation

Model Configuration

• Input Dimension: 3013
• Latent Dimension: 3013
• Number of Classes: 17
• Hidden Dimension: 128
• Dropout: 0.3

Usage

1. Install the required dependencies:
   pip install torch numpy